AI ‐Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI ‐ RADS v2.1: Performance Comparison Between Novice‐Level and Experienced Readers

Background Despite promising results of artificial intelligence (AI) in prostate cancer (PCa) detection, its impact on biparametric MRI (bpMRI) interpretation remains uncertain, especially for readers with limited experience. Purpose To evaluate the effect of AI software assistance on prostate bpMRI interpretation by readers with different levels of prostate MRI experience. Study Type Retrospective. Population Six hundred and forty‐six male patients, including 297 with PCa. Field Strength/Sequence 3.0 T; T2 ‐weighted imaging using fast spin echo sequence, diffusion‐weighted imaging using single‐shot echo‐planar imaging. Assessment Two experienced readers (8 and 10 years of prostate MRI experience) and two novice‐level readers (2 years of general radiology experience; 20–50 prior prostate MRI cases) assessed all examinations twice, without and with AI software (uAI, United Imaging) assistance, in counterbalanced orders with a 4‐week washout interval. Lesions were scored using Prostate Imaging Reporting and Data System (PI‐RADS) v2.1 at ≥ 3 and ≥ 4 thresholds. Histopathology was the reference standard. The primary analysis defined cancer as International Society of Urological Pathology (ISUP) grade group ≥ 1 (Gleason score ≥ 6); sensitivity analysis defined clinically significant cancer as ISUP grade group ≥ 2. Statistical Tests Generalized Estimating Equations were used for clustered data. Receiver operating characteristic (ROC) analysis with the Obuchowski–Rockette model was used to compare the area under the ROC curve (AUC). Cohen's κ assessed inter‐reader agreement; two‐sided p < 0.05 indicated significance. Results For ISUP ≥ 1, uAI increased novice‐level/experienced‐reader AUCs (0.684–0.744; 0.757–0.794). At PI‐RADS ≥ 3, novice‐level sensitivity/specificity significantly improved (0.71–0.79; 0.46–0.58). Experienced‐reader sensitivity gains were nonsignificant ( p = 0.344/0.291). For ISUP ≥ 2 at ≥ 3, all‐reader sensitivity/specificity increased (0.76–0.82; 0.47–0.57). Novice‐level κ increased at ≥ 3/≥ 4 (0.582–0.700; 0.654–0.741). Data Conclusion uAI assistance improved diagnostic performance, with multi‐metric improvements in novice‐level readers. Level of Evidence 2. Technical Efficacy Stage 3.